VYPR
Moderate severityNVD Advisory· Published Sep 16, 2022· Updated Apr 23, 2025

`CHECK` fail in `ParameterizedTruncatedNormal` in TensorFlow

CVE-2022-35984

Description

TensorFlow is an open source platform for machine learning. ParameterizedTruncatedNormal assumes shape is of type int32. A valid shape of type int64 results in a mismatched type CHECK fail that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 72180be03447a10810edca700cbc9af690dfeb51. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

Affected packages

Versions sourced from the GitHub Security Advisory.

PackageAffected versionsPatched versions
tensorflowPyPI
< 2.7.22.7.2
tensorflowPyPI
>= 2.8.0, < 2.8.12.8.1
tensorflowPyPI
>= 2.9.0, < 2.9.12.9.1
tensorflow-cpuPyPI
< 2.7.22.7.2
tensorflow-cpuPyPI
>= 2.8.0, < 2.8.12.8.1
tensorflow-cpuPyPI
>= 2.9.0, < 2.9.12.9.1
tensorflow-gpuPyPI
< 2.7.22.7.2
tensorflow-gpuPyPI
>= 2.8.0, < 2.8.12.8.1
tensorflow-gpuPyPI
>= 2.9.0, < 2.9.12.9.1

Affected products

1

Patches

1
72180be03447

Fix tensor shape dtype bug in parameterized_truncated_normal.

https://github.com/tensorflow/tensorflowAntonio SanchezJun 23, 2022via ghsa
2 files changed · +29 7
  • tensorflow/core/kernels/parameterized_truncated_normal_op.cc+6 7 modified
    @@ -32,6 +32,7 @@ limitations under the License.
     #include "tensorflow/core/framework/register_types.h"
     #include "tensorflow/core/framework/tensor.h"
     #include "tensorflow/core/framework/tensor_shape.h"
    +#include "tensorflow/core/framework/tensor_util.h"
     #include "tensorflow/core/kernels/stateless_random_ops.h"
     #include "tensorflow/core/lib/random/random_distributions.h"
     #include "tensorflow/core/platform/logging.h"
    @@ -630,20 +631,18 @@ class ParameterizedTruncatedNormalOp : public OpKernel {
         OP_REQUIRES(ctx, shape_tensor.NumElements() > 0,
                     errors::InvalidArgument("Shape tensor must not be empty, got ",
                                             shape_tensor.DebugString()));
    -    int32_t num_batches = shape_tensor.flat<int32>()(0);
    +    TensorShape tensor_shape;
    +    OP_REQUIRES_OK(ctx, tensor::MakeShape(shape_tensor, &tensor_shape));
     
    +    int32_t num_batches = tensor_shape.dim_size(0);
         int32_t samples_per_batch = 1;
    -    const int32_t num_dims = shape_tensor.dim_size(0);
    +    const int32_t num_dims = tensor_shape.dims();
         for (int32_t i = 1; i < num_dims; i++) {
    -      samples_per_batch *= shape_tensor.flat<int32>()(i);
    +      samples_per_batch *= tensor_shape.dim_size(i);
         }
         const int32_t num_elements = num_batches * samples_per_batch;
     
         // Allocate the output before fudging num_batches and samples_per_batch.
    -    auto shape_vec = shape_tensor.flat<int32>();
    -    TensorShape tensor_shape;
    -    OP_REQUIRES_OK(ctx, TensorShapeUtils::MakeShape(
    -                            shape_vec.data(), shape_vec.size(), &tensor_shape));
         Tensor* samples_tensor;
         OP_REQUIRES_OK(ctx, ctx->allocate_output(0, tensor_shape, &samples_tensor));
     
    
  • tensorflow/python/kernel_tests/random/parameterized_truncated_normal_op_test.py+23 0 modified
    @@ -303,6 +303,29 @@ def testSamplingWithSmallStdDevFarFromBound(self):
           self.assertAllGreater(samples, 0.)
           self.assertAllGreater(samples_stateless, 0.)
     
    +  def testShapeTypes(self):
    +    for shape_dtype in [np.int32, np.int64]:
    +      shape = np.array([1000], dtype=shape_dtype)
    +      sample_op = random_ops.parameterized_truncated_normal(
    +          shape=shape, means=0.0, stddevs=0.1, minvals=-1., maxvals=1.)
    +      new_seed = random_ops.random_uniform([2],
    +                                           seed=1234,
    +                                           minval=0,
    +                                           maxval=(2**31 - 1),
    +                                           dtype=np.int32)
    +      sample_op_stateless = stateless.stateless_parameterized_truncated_normal(
    +          shape=shape,
    +          seed=new_seed,
    +          means=0.0,
    +          stddevs=0.1,
    +          minvals=-1.,
    +          maxvals=1.)
    +
    +      samples = self.evaluate(sample_op)
    +      stateless_samples = self.evaluate(sample_op_stateless)
    +      self.assertAllEqual(samples.shape, shape)
    +      self.assertAllEqual(stateless_samples.shape, shape)
    +
       def testStatelessParameterizedTruncatedNormalHasGrads(self):
         mean = variables.Variable(0.01)
         stddev = variables.Variable(1.)
    

Vulnerability mechanics

Generated by null/stub on May 9, 2026. Inputs: CWE entries + fix-commit diffs from this CVE's patches. Citations validated against bundle.

References

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